IIT Public Safety Off-Campus Ride Service Optimization

Authors

  • Sanaz Kazemzadehazad Illinois Institute of Technology

DOI:

https://doi.org/10.18409/3svrz080

Keywords:

Public safety escorts, Qualitative Interviews, Route optimization, Equity index

Abstract

The Mies Campus of Illinois Institute of Technology (IIT) is in the high-crime area of Bronzeville in Chicago, with a crime rate significantly higher than the national average. According to anecdotal sources, students report feeling embarrassed due to long waits for off-campus ride services and a lack of equitable resource allocation. This concern is particularly pressing for students who stay late on campus for classes, studying, or project deadlines and must resort to alternative means of transportation such as ride-hailing platforms (e.g., Lyft and Uber) or walking, which can be, respectively, financially burdensome and risky in high-crime areas. Limited pickup locations, an inequitable first-come, first-served system, and uneven fleet distribution exacerbate these issues, leaving many students feeling unsafe.

Despite the efforts and efficiency of the Department of Public Safety (DPS), there is room for improvement in its performance regarding student ride services, as many students face significant challenges during late-night commutes when they are unable to use DPS services. Therefore, IIT should take on the ethical responsibility of addressing the challenges faced by students commuting to the Mies campus.

This project aims to identify the gaps in the DPS ride services by conducting a qualitative study and interviews to gain a deeper understanding of students’ experiences and opinions. After extracting the sub-indicators in each challenge category, relevant solutions and equity indexes are provided to help the university in the development of a fair and effective system for allocating public safety resources.

References

Downloads

Published

2025-12-18

Issue

Section

SoReMo Fellow Projects

How to Cite

IIT Public Safety Off-Campus Ride Service Optimization. (2025). Socially Responsible Modeling, Computation, and Design, 5(1). https://doi.org/10.18409/3svrz080